Genomic characterisation of the indigenous Irish Kerry cattle breed

Browett, S, McHugo, G, Richardson, IW, Magee, DA, Park, SDE, Fahey, AG, Kearney, JF, Correia, CN, Randhawa, IAS and MacHugh, DE

http://dx.doi.org/10.3389/fgene.2018.00051

Title Genomic characterisation of the indigenous Irish Kerry cattle breed

Authors Browett, S, McHugo, G, Richardson, IW, Magee, DA, Park, SDE, Fahey, AG, Kearney, JF, Correia, CN, Randhawa, IAS and MacHugh, DE

Type Article

URL This version is available at: http://usir.salford.ac.uk/id/eprint/46300/

Published Date 2018

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ORIGINAL RESEARCHpublished: 19 February 2018

doi: 10.3389/fgene.2018.00051

Frontiers in Genetics | www.frontiersin.org 1 February 2018 | Volume 9 | Article 51

Edited by:

Ino Curik,

Faculty of Agriculture, University of

Zagreb, Croatia

Reviewed by:

Gábor Mészáros,

University of Natural Resources and

Life Sciences, Vienna, Austria

Paolo Ajmone Marsan,

Università Cattolica del Sacro Cuore,

Italy

*Correspondence:

David E. MacHugh

david.machugh@ucd.ie

Specialty section:

This article was submitted to

Livestock Genomics,

a section of the journal

Frontiers in Genetics

Received: 03 September 2017

Accepted: 02 February 2018

Published: 19 February 2018

Citation:

Browett S, McHugo G,

Richardson IW, Magee DA, Park SDE,

Fahey AG, Kearney JF, Correia CN,

Randhawa IAS and MacHugh DE

(2018) Genomic Characterisation of

the Indigenous Irish Kerry Cattle

Breed. Front. Genet. 9:51.

doi: 10.3389/fgene.2018.00051

Genomic Characterisation of theIndigenous Irish Kerry Cattle Breed

Sam Browett 1, Gillian McHugo 2, Ian W. Richardson 3, David A. Magee 2,

Stephen D. E. Park 3, Alan G. Fahey 2, John F. Kearney 4, Carolina N. Correia 2,

Imtiaz A. S. Randhawa 5 and David E. MacHugh 2,6*

1 Ecosystems and Environment Research Centre, School of Environment and Life Sciences, University of Salford, Salford,

United Kingdom, 2 Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin,

Dublin, Ireland, 3 IdentiGEN Ltd., Dublin, Ireland, 4 Irish Cattle Breeding Federation, Bandon, Ireland, 5 Sydney School of

Veterinary Science, The University of Sydney, Camden, NSW, Australia, 6UCD Conway Institute of Biomolecular and

Biomedical Research, University College Dublin, Dublin, Ireland

Kerry cattle are an endangered landrace heritage breed of cultural importance to

Ireland. In the present study we have used genome-wide SNP array data to evaluate

genomic diversity within the Kerry population and between Kerry cattle and other

European breeds. Patterns of genetic differentiation and gene flow among breeds

using phylogenetic trees with ancestry graphs highlighted historical gene flow from the

British Shorthorn breed into the ancestral population of modern Kerry cattle. Principal

component analysis (PCA) and genetic clustering emphasised the genetic distinctiveness

of Kerry cattle relative to comparator British and European cattle breeds. Modelling of

genetic effective population size (Ne) revealed a demographic trend of diminishing Ne

over time and that recent estimated Ne values for the Kerry breed may be less than

the threshold for sustainable genetic conservation. In addition, analysis of genome-wide

autozygosity (FROH) showed that genomic inbreeding has increased significantly during

the 20 years between 1992 and 2012. Finally, signatures of selection revealed genomic

regions subject to natural and artificial selection as Kerry cattle adapted to the climate,

physical geography and agro-ecology of southwest Ireland.

Keywords: cattle, conservation genomics, endangered breed, inbreeding, genetic diversity, population genomics,

selection signature, single nucleotide polymorphism

INTRODUCTION

Approximately 10,000 years ago, humans first domesticated wild aurochs (Bos primigenius)—theprogenitor of modern cattle—in the Fertile Crescent region of Southwest Asia (Larson and Fuller,2014; Larson et al., 2014; MacHugh et al., 2017). Extant domestic cattle, which encompass humplesstaurine (B. taurus), humped zebu (B. indicus) and myriad B. taurus/indicus hybrid populations,have, through genetic drift and natural and artificial selection, diversified into more than 1,100recognised breeds. However, beginning in the middle of the twentieth century, socioeconomicpreferences for large highly productive dairy, beef and dual-purpose breeds have led to extinctionand increased vulnerability of more than 200 locally-adapted landrace or native cattle breeds(Gandini et al., 2004; Food and Agriculture Organization, 2007, 2015).

With the advent of accelerating climate change, particularly in the Arctic and circumarcticregions (Vihma, 2014; Gao et al., 2015), agro-ecological environments in north-western Europe

Browett et al. Genomic Characterisation of the Kerry Cattle Breed

will inevitably undergo significant change during the comingcentury (Smith and Gregory, 2013; Wheeler and von Braun,2013). It is, therefore, increasingly recognised that long-termsustainability of animal production systems and food security willnecessitate conservation and management of livestock geneticresources in this region (Hoffmann, 2010; Boettcher et al., 2015;Kantanen et al., 2015). Locally-adapted native livestock breedswith distinct microevolutionary histories and minimal externalgene flow will have accumulated novel genomic variation andhaplotype combinations for quantitative health, fertility andproduction traits (Hill, 2014; Felius et al., 2015; Kristensen et al.,2015). These populations may therefore be key to future breedingprogrammes directed towards adaptation of European livestockto new agro-ecological and production environments (Biscariniet al., 2015; Boettcher et al., 2015; Phocas et al., 2016a,b).

The availability of powerful and cheap tools for genotypinglarge numbers of single nucleotide polymorphisms (SNPs)has provided conservation biologists and animal geneticistswith the opportunity to characterise genomic variation andestimate population genetic parameters at very high resolutionin threatened or endangered livestock breeds (Pertoldi et al.,2014; Ben Jemaa et al., 2015; Beynon et al., 2015; Mészároset al., 2015; Burren et al., 2016; Decker et al., 2016; Iso-Touru et al., 2016; Manunza et al., 2016; Mastrangelo et al.,2016; Visser et al., 2016; Williams et al., 2016; François et al.,2017). These studies are already providing important baselinedata for genetic conservation and will underpin programmesfor managed breeding and biobanking of these populations(Groeneveld et al., 2016).

As a native breed with a claimed ancient heritage, Kerry cattleare considered culturally important to Ireland (Curran, 1990).It is a landrace cattle population that remains productive inharsh upland regions with poor quality feed, which are typicalof southwest Ireland where the Kerry breed evolved (Food andAgriculture Organization, 2017). These cattle were often referredto anecdotally in Ireland as the “poor man’s cow” due to theirability to produce relatively large quantities of milk on very sparsefodder; the Kerry breed is also considered to be a remnant of whatwas once a substantially larger and more widespread historicalpopulation. Levels of inbreeding have been estimated usingpedigree data and the accumulated figure since the foundationof the herd book in 1887 reached 15% in 1985 (O’hUigín andCunningham, 1990).

In recent decades the Kerry cattle breed has experiencedsignificant population fluctuations due to changingsocioeconomic and agricultural circumstances. During the1980s, the number of breeding females decreased to less than200, prompting the Irish agricultural authorities to introducea Kerry cattle conservation scheme (McParland, 2013), whichhas continued to the present day in the form of the Departmentof Agriculture, Food and the Marine (DAFM) Kerry CattlePremium Scheme (Department of Agriculture Food and theMarine, 2017).

The formal conservation policy and supports initiated duringthe early 1990s led to a significant increase in the Kerry cattlepopulation, such that by 2007 the number of breeding femaleshad increased to more than a thousand animals (Food and

Agriculture Organization, 2007). In recent years, however, dueto deteriorating economic circumstances in Ireland post-2008,the Kerry cattle population has substantially declined once againand is classified as endangered and under significant threat ofextinction or absorption through crossbreeding with other breeds(McParland, 2013; Department of Agriculture Food and theMarine, 2014).

The Kerry cattle breed was one of the first European heritagecattle breeds to be surveyed using molecular population geneticstechniques. We have previously used autosomal microsatellitegenetic markers and mitochondrial DNA (mtDNA) controlregion sequence variation for comparative evolutionary studiesof genetic diversity in Kerry cattle and other British, European,African and Asian breeds (MacHugh et al., 1997, 1998,1999; Troy et al., 2001). In addition, Bray et al. haveused microsatellites to examine admixture and ancestry inKerry cattle and the Dexter and Devon breeds (Bray et al.,2009). Results from these studies demonstrated that Kerrycattle exhibit markedly low mtDNA sequence diversity, butautosomal microsatellite diversity comparable to other cattlebreeds native to Britain and Ireland. More recently, analysesof medium- and high-density SNP genotypes generated usinggenome sequence data from an extinct British B. primigeniussubfossil have shown that Kerry cattle retain a significantgenomic signature of admixture from wild aurochs (Parket al., 2015; Upadhyay et al., 2017). This observation highlightsthe genetic distinctiveness of the Kerry population and hasmajor implications for conservation and management of thebreed.

For the present study, and within a genetic conservationframework, we performed high-resolution comparativepopulation genomics analyses of Kerry cattle and a range ofBritish and European cattle breeds. These analyses encompassedphylogenetic network reconstruction, evaluation of geneticstructure and inbreeding, modelling of historical effectivepopulation sizes and functional analyses of artificial and naturalselection across the Kerry genome.

MATERIALS AND METHODS

Kerry Cattle Population DNA Sampling in1991/92 and 2011/12Two different population samples from the Irish Kerry cattlebreed were used for this study (Figure 1). The first populationsample consisted of peripheral blood and semen straw genomicDNA collected and purified from 36 male and female Kerry cattlein 1991/92, which are a subset of the Kerry cattle populationsample (n = 40) we have previously described and used formicrosatellite-based population genetics analyses (MacHughet al., 1997, 1998). Pedigree records and owners were consultedto ensure that a representative sample of animals was obtained.This Kerry population sample group is coded as KY92.

The second Kerry cattle population sample was collectedin 2011/12 from 19 different herds located across southernand western Ireland. Performagene (PG-100) nasal swab DNAcollection kits were used for biological sample collection (DNA

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Browett et al. Genomic Characterisation of the Kerry Cattle Breed

FIGURE 1 | Photograph of a Kerry cow and locations of Kerry cattle herd DNA sampling in southern Ireland. The area of each circle corresponds to the size of each

population sample. Dark green = animals sampled during 1991/92 (KY92); light green = animals sampled during 2011/12 (KY12). Kerry cow image is copyright of the

Kerry Cattle Society Ltd.

Genotek Inc., Ottawa, Canada). Nasal swab DNA samples werecollected from a total of 75 male and female Kerry cattle andowners were consulted to ensure that a representative sampleof animals was obtained. This Kerry population sample groupis coded as KY12. Genomic DNA was purified from 0.5ml ofeach PG-100 nasal swab sample using the laboratory protocolrecommended by the manufacturer (DNA Genotek Inc.).

SNP Genotyping and Assembly ofComparative SNP Data SetsIllumina R© Bovine SNP50 BeadChip (Matukumalli et al., 2009)genotyping on all 111 Kerry genomic DNA samples (KY92and KY12 sample panels plus nine blinded sample duplicatesfor quality control purposes) was performed by WeatherbysScientific (Co. Kildare, Ireland).

For comparative population genomics analyses, equivalentSNP data for a range of other breeds were obtained frompreviously published work (Decker et al., 2009; Flori et al., 2009;Gibbs et al., 2009; Matukumalli et al., 2009; Gautier et al., 2010;Park et al., 2015). The breed SNP data were split into two discretecomposite data sets: a European breed SNP data set (EU) and aSNP data set for a subset of European breeds originating fromBritain and Ireland (BI). A population sample of West AfricanN’Dama B. taurus cattle from Guinea (NDAM) was also usedas an outgroup for the phylogenetic analyses. Table 1 providesdetailed biogeographical information on the cattle breed samplesused for the present study.

Sample Removal and Quality Control andFiltering of SNPsGenomic non-exclusion—in other words, genome-wide SNPprofiles completely compatible with parent-offspring relationship(with allowance for very low-level genotyping error)—were usedto identify animals from the KY92 and KY12 population samplesthat were parent-offspring pairs. One of the two animals in eachpair was then randomly removed to generate the working SNPdata set. Following this procedure, quality control and filteringbased on recorded SNP genotypes was performed as detailedbelow for the EU and BI data sets.

Prior to quality control and filtering there were 54,057 SNPsin the EU data set (608 animals, including KY92 and KY12)and in the BI data set (354 animals, including KY92 and KY12).SNP quality filtering was performed using PLINK version 1.07(Purcell et al., 2007), such that individual SNPs with more than10% missing data and a minor allele frequency (MAF) of ≤0.01(1%) were removed from both data sets; however, for analyses ofgenomic inbreeding and runs of homozygosity (ROH) the MAFfiltering threshold was not imposed. Only autosomal SNPs wereretained and individual animal samples with a SNP call rate lessthan 90% were also removed from each of the two data sets.

SNP quality control and filtering were performed acrossbreeds/populations (by data set) for construction of phylogeniesand ancestor graphs, multivariate analysis, investigation ofpopulation structure and detection of signatures of selection.For intrapopulation analyses of effective population size (Ne)

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TABLE 1 | Cattle breed/population samples used for the present study.

Breed/population Code Data set Breed purpose Country of origin Source

Angus ANGU BI/EU Beef Scotland 1, 2

Belted Galloway BGAL BI Beef Scotland 2

British Shorthorn BSHN BI/EU Dual purpose England 2

Brown Swiss BRSW EU Dairy Switzerland 1, 2, 3

Charolais CHAR EU Beef France 1, 2, 3

Devon DEVN BI Beef England 2

Dexter DXTR BI Dual purpose Ireland 2

English Longhorn ELHN BI Beef England 2

Finnish Ayrshire FAYR BI/EU Dairy Scotland/Finland 2

Galloway GALL BI Beef Scotland 2

Gelbvieh GELB EU Dual purpose Germany 2, 4

Guernsey GNSY BI/EU Dairy Channel Islands 1, 2

Hereford HRFD BI/EU Beef England 1, 2

Holstein HOLS EU Dairy The Netherlands 1, 2, 5

Jersey JRSY BI/EU Dairy Channel Islands 1, 2, 3

Kerry sampled 1991/92 KY92 BI/EU Dairy Ireland Current

Kerry sampled 2011/12 KY12 BI/EU Dairy Ireland Current

Limousin LIMS EU Beef/draft France 1, 2

Lincoln Red LNCR BI Beef England 2

Montbeliarde MONT EU Dairy France 2, 5

N’Dama NDAM – Dual purpose Guinea (West Africa) 1

Norwegian Red NRED EU Dairy Norway 1

Piedmontese PDMT EU Dual purpose Italy 1, 2

Red Angus RANG BI/EU Beef Scotland 1

Red Poll REDP BI Beef England 2

Romagnola ROMG EU Beef/draft Italy 1

Scottish Highland SCHL BI Beef Scotland 2

Simmental SIMM EU Dual purpose/draft Switzerland 2, 4

South Devon SDEV BI Beef England 2

Sussex SUSX BI Beef/draft England 2

Welsh Black WBLK BI Dual purpose Wales 2

White Park WHPK BI Dual purpose/draft England 2

1Gibbs et al. (2009); 2Decker et al. (2009); 3Gautier et al. (2010); 4Matukumalli et al. (2009); 5Flori et al. (2009).

and genomic inbreeding, all SNPs genotyped (54,057) werefiltered within breeds/populations as detailed above. However,an additional filtering procedure was used to remove SNPsdeviating from Hardy-Weinberg equilibrium (HWE) with a P-value threshold of < 0.0001. Also, for the Ne analysis, a morestringent MAF threshold of 0.05 was used.

Generation of Identity-by-State (IBS) MatrixUsing the filtered genome-wide SNP data, PLINK v1.07 was alsoused to generate identity-by-state (IBS) values for all pairs ofKerry cattle (KY92 and KY12), including the nine blinded sampleduplicates for sample verification and tracking purposes.

Construction of Phylogenetic Trees andAncestry GraphsMaximum likelihood (ML) phylogenetic trees withancestry graphs were generated for the EU and BI datasets using the TreeMix (version 1.12) software package(Pickrell and Pritchard, 2012). The West African B. taurus

NDAM breed sample (n= 22) was used as an outgroup. TreeMixwas run without using SNP blocks (as described in the TreeMixsoftware documentation) and ML phylogenetic trees weregenerated with no migration edges (m = 0) up to ten migrationedges (m= 10).

Population Differentiation and GeneticStructureTo visualise the main axes of genomic variation among cattlebreeds and individual animals, multivariate principal componentanalysis (PCA) was performed for the composite EU and BISNP data sets using SMARTPCA from the EIGENSOFT package(version 4.2) with default settings (Patterson et al., 2006).

To further investigate genetic structure and admixture historyfor Kerry cattle and other breeds the fastSTRUCTURE softwarepackage (Raj et al., 2014) was used to analyse the EU andBI data sets for a range of K possible ancestral populations(K = 2–15). For the present study, the simple prior approachdescribed by Raj et al. (2014) was used, which is sufficient for

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modelling population/breed divergence. To identify the “true”K-value for the number of ancestral populations, a series offastSTRUCTURE runs with pre-defined K-values were examinedusing the chooseK.py script (Raj et al., 2014). Outputs from thefastSTRUCTURE analyses were visualised using the DISTRUCTsoftware program (Rosenberg et al., 2002) using standardparameters.

Modelling Current and Historical EffectivePopulation Size (Ne)Current and historical Ne trends were modelled with genome-wide SNP linkage disequilibrium (LD) data for the KY92 andKY12 populations plus a selection of BI and EU breeds using theSNeP software tool as described by Barbato et al. (2015). Thismethod facilitates estimation of historical Ne values from SNPlinkage disequilibrium (LD) data using the following equation(Corbin et al., 2012):

NT(t) =(

4f (ct))−1

(

E[

r2adj|ct

]−1− α

)

where NT is the effective population size t generations ago

calculated as t =(

2f (ct))−1

(Hayes et al., 2003), ct is therecombination rate defined for a specific physical distancebetween SNP markers, r2

adjis the LD value adjusted for sample

size and the recommended default α value= 1 was used to correctfor the occurrence of mutation (Barbato et al., 2015). In addition,the SNeP program option for unphased SNP data was used forthe analyses described here.

Evaluation of Genomic Inbreeding andRuns of Homozygosity (ROH)Individual animal genomic inbreeding was evaluated as genome-wide autozygosity estimated from SNP data using ROH and theFROH statistic introduced by McQuillan et al. (2008). The FROHstatistic was calculated as the ratio of the total length of definedROH (LROH) to the total length of the autosomal genome coveredby SNPs:

FROH =

∑

LROH

LAUTO

PLINK v1.07 was used to define ROH using a slidingwindow approach and procedures modified from previousrecommendations for Illumina R© Bovine SNP50 BeadChip andsimilar SNP data sets (Purfield et al., 2012, 2017). The criteriafor defining individual ROH were set such that the ROH wasrequired to be at least 500 kb in length, with a minimumdensity of one SNP per 120 kb and that there was a gap ofat least 1,000 kb between each ROH. A sliding window of 50SNPs was incrementally advanced one SNP at a time along thegenome; each discrete window could contain a maximum of oneheterozygous SNP and no more than two SNPs with missinggenotypes. Following Purfield et al. (2012) all filtered genomicSNPs (without a MAF threshold), including those located incentromeric regions, were used to estimate FROH values forindividual animals.

Genome-Wide Detection of Signatures ofSelection and Functional EnrichmentAnalysisIn the absence of hard selective sweeps, single selection testsusing high-density SNP data do not perform well in detectingsignatures of selection from individual livestock breeds (Kemperet al., 2014). Therefore, for the present study, genomic signaturesof selection were identified using the composite selection signal(CSS) method introduced by Randhawa et al. (2014). The CSSmethod has been shown to be a robust and sensitive approachfor detecting genomic signatures of selection underlyingmicroevolution of complex traits in livestock (Randhawa et al.,2015). The CSS is a weighted index of signatures of selectionfrom multiple estimates; it is a nonparametric procedure thatuses fractional ranks of constituent tests and does not dependon assumptions about the distributions of individual testresults.

As described in detail by Randhawa et al. (2014), the CSSmethod can be used to combine the fixation index (FST), thechange in selected allele frequency (1SAF) and the cross-population extended haplotype homozygosity (XP-EHH) testsinto one composite statistic for each SNP in a populationgenomics data set. For the present study, we used 36,621 genome-wide SNPs genotyped in 98 individual Kerry cattle samples (fromboth the KY92 and KY12 populations) and a sample of 102randomly selected cattle (six random cattle from each breed ofthe EU data set). To mitigate against false positives, genomicselection signatures were only considered significant if at leastone SNP from the set of the top 0.1% genome-wide CSS scoreswas flanked by at least five SNPs from the set of the top 1% CSSscores.

The Ensembl BioMart data mining resource (Smedley et al.,2015) was used to identify genes within ± 1.0Mb of eachselection peak (Ensembl release 90, August 2017). Followingthis, Ingenuity R© Pathway Analysis (IPA R©: Qiagen, RedwoodCity, CA, USA; release date June 2017) was used to performan overrepresentation enrichment analysis (ORA) with this geneset to identify canonical pathways and functional processes ofbiological importance. The total gene content of Ensembl release90 version of the UMD3.1 bovine genome assembly (Zimin et al.,2009) was used as the most appropriate reference gene set forthese analyses (Timmons et al., 2015).

RESULTS AND DISCUSSION

Sample Removal and SNP Filtering andQuality ControlGenomic non-exclusion identified 20 parent-offspring pairsfrom the KY12 population and one sample from each pairwas randomly removed (sample codes: KY12_01, KY12_05,KY12_13, KY12_14, KY12_17, KY12_18, KY12_19, KY12_46,KY12_55, KY12_67). Thereafter, general SNP quality control andfiltering led to additional samples being excluded (KY12_26,KY12_28 and KY12_54), giving a total filtered KY12 populationsample of 62 animals for downstream population genomicsanalyses.

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After SNP quality control and filtering across the twocomposite data sets (EU and BI), there were 36,621 autosomalSNPs from 605 individual animals in the EU data set andthere were 37,395 autosomal SNPs from 351 animals in theBI data set. When the West African NDAM breed sample(n = 22) was included for the ML phylogenetic tree andancestry graph analyses, the number of SNPs used was 36,000from 627 animals for the EU data set and 37,490 from 373animals for the BI data set. The final numbers of SNPs usedfor individual breed/population analyses of Ne and genomicinbreeding after all quality control and filtering (includingadditional filtering for deviations from HWE) are shown inTable 2.

All data sets, including EU and BI composite data setsand individual breed/population data sets had total SNPcall rates of > 99%. The IBS values estimated for Kerrycattle (KY92 and KY12) from filtered genome-wide SNPdata are reported in Supplementary Table 1 and described

further in section Genomic Relationship and Analysis ofInbreeding.

Observed Heterozygosity (Ho) Estimatedfrom Genome-Wide SNP DataTable 2 provides genome-wide Ho values for each of thebreeds/populations used for the present study. The lowestgenome-wide Ho value was observed for the West AfricaNDAM B. taurus breed, which is likely a consequence ofascertainment bias introduced by a focus on polymorphic SNPsin European B. taurus during design of the Illumina R© BovineSNP50 BeadChip (Matukumalli et al., 2009).

Generally, as shown in Table 2 for the EU and BI breeds andpopulations, local landrace or heritage breeds display lower Ho

values compared to more widespread production breeds such asthe Simmental (SIMM), Holstein (HOLS) or Charolais (CHAR)breeds. In addition, as might be expected, production breeds

TABLE 2 | Breed/population sample size, observed heterozygosity and SNP filtering information.

Breed/population Code Data set Sample size (n)

post-filtering

Observed

heterozygosity Ho

No. SNPs Ne

modelling

No. SNPs genomic

inbreeding

Angus ANGU BI/EU 72 0.3048 31,413 39,576

Belted Galloway BGAL BI 4 0.2902 25,997 39,582

British Shorthorn BSHN BI/EU 10 0.2549 29,038 39,576

Brown Swiss BRSW EU 31 0.2894 – –

Charolais CHAR EU 48 0.3209 – –

Devon DEVN BI 4 0.2859 – –

Dexter DXTR BI 4 0.2458 24,753 37,903

English Longhorn ELHN BI 3 0.2232 – –

Finnish Ayrshire FAYR BI/EU 7 0.3064 – –

Galloway GALL BI 4 0.2942 – –

Gelbvieh GELB EU 8 0.3125 – –

Guernsey GNSY BI/EU 19 0.2764 – 50,323

Hereford HRFD BI/EU 35 0.2964 – 39,535

Holstein HOLS EU 70 0.3192 36,152 43,135

Jersey JRSY BI/EU 44 0.2718 31,358 43,136

Kerry sampled 1991/92 KY92 BI/EU 36 0.2965 37,556 51,731

Kerry sampled 2011/12 KY12 BI/EU 62 0.3042 36,428 50,756

Limousin LIMS EU 45 0.3122 – –

Lincoln Red LNCR BI 7 0.2789 26,350 34,173

Montbeliarde MONT EU 31 0.3019 – –

N’Dama NDAM – 22 0.2158 – –

Norwegian Red NRED EU 20 0.3190 – –

Piedmontese PDMT EU 23 0.3240 – –

Red Angus RANG BI/EU 14 0.3092 – –

Red Poll REDP BI 5 0.2905 – –

Romagnola ROMG EU 21 0.2943 – –

Scottish Highland SCHL BI 8 0.2823 – –

Simmental SIMM EU 9 0.3136 – –

South Devon SDEV BI 3 0.3070 – –

Sussex SUSX BI 4 0.2792 – –

Welsh Black WBLK BI 2 0.3203 – –

White Park WHPK BI 4 0.2270 – –

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originally derived from minor island populations (Jersey [JRSY]and Guernsey [GNSY]) also exhibit relatively low Ho values. Inthe context of genetic conservation it is therefore encouragingthat the KY92 and KY12 population samples display intermediateHo values that are at the upper end of the range observed for theheritage breeds.

Maximum Likelihood PhylogeneticAncestry Graphs Using Genome-Wide SNPDataTo examine microevolutionary patterns of genetic differentiationand gene flow among cattle breeds and populations, MLphylogenetic ancestry graphs were generated using TreeMix.For the EU data set, the ML tree topology was consistentfor all values of m, with the exception of m = 2 migrationedges, where the Hereford breed (HRFD) was observed to group

with the HOLS breed. The ML tree generated with m = 5 isshown in Figure 2, which highlights the genetic similarity of theNorthern European breeds (British, Irish and Scandinavian). Asexpected the two Kerry population samples (KY92 and KY12)are genetically very similar and emerge on the same branch asthe HRFD breed. It is also noteworthy that there is a high-weightmigration edge between the British Shorthorn breed (BSHN) andthe root of the two Kerry population samples, supporting thehypothesis of historical gene flow from the British Shorthornbreed into the ancestral population of modern Kerry cattle(Curran, 1990).

For the ML trees generated using the BI data set,breed/population differentiation was less apparent, possibly dueto similar biogeographical origins for these breeds and/or smallersample sizes for some of the populations sampled. Figure 3 showsthe ML tree generated with m = 5 for the BI data set. For m = 5,all migration edges stem from the BSHN/Lincoln Red (LNCR)

FIGURE 2 | Maximum likelihood (ML) phylogenetic tree network graph with five migration edges (m = 5) generated for genome-wide SNP data (36,000 autosomal

SNPs) from European cattle breeds (EU data set). The West African taurine N’Dama breed sampled in Guinea is included as a population outgroup. Coloured lines

and arrows show migration edges that model gene flow between lineages with different migration weights represented by the colour gradient.

FIGURE 3 | Maximum likelihood (ML) phylogenetic tree network graph with five migration edges (m = 5) generated for genome-wide SNP data (37,490 autosomal

SNPs) from cattle breeds of British and Irish origin (BI data set). The West African taurine N’Dama breed sampled in Guinea is included as a population outgroup.

Coloured lines and arrows show migration edges that model gene flow between lineages with different migration weights represented by the colour gradient.

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branch, including a medium-weight migration edge connectingto the Kerry cattle branch. These results support the hypothesisthat there was significant gene flow during the eighteenth andnineteenth centuries from British Shorthorn cattle into theancestral populations for a range of modern British and Irishcattle breeds (Grobet et al., 1998; Felius et al., 2011, 2015).

Multivariate Principal Component Analysisof Genome-Wide SNP DataTo investigate inter- and intra-population genomic diversity andgenetic relationship among individual animals from multiplecattle breeds and populations, PCA was performed usinggenome-wide SNP data. Principal component plots of the first(PC1) and second (PC2) principal components are shown inFigures 4, 5 for the EU and BI data sets, respectively.

In Figure 4, for the EU data set, PC1 and PC2 account for18.2% and 16.8% of the total variation for PC1–10, respectively.The PC1 plot axis differentiates the British Angus (ANGU),Red Angus (RANG) and BSHN and Irish KY92 and KY12populations from the rest of the European breeds, including theBritish HRFD and GNSY and JRSY Channel Islands breeds. Inaddition, the ANGU and RANG and the Kerry (KY92 and KY12)emerge at the opposite extremes of the PC2 plot axis. In Figure 5,for the BI data set, PC1 and PC2 account for 23.7% and 22.7%of the total variation for PC1–10, respectively. The PC1 plotaxis recapitulates PC2 in Figure 4 and differentiates the Kerry(KY92 and KY12) from the ANGU and RANG breeds with theother British breeds emerging between these two extremes. Theseresults highlight the genetic distinctiveness of the Kerry cattle

breed in comparison to a wide range of British production andheritage landrace cattle breeds and support their status as animportant cattle genetic resource that should be prioritised forconservation.

The PC2 plot axis in Figure 5 differentiates the HRFD breedfrom the other British and Irish breeds and reveals substantialgenetic diversity among individual HRFD animals. However, inthis context, it is important to note that the pattern of geneticdiversity revealed here for the HRFD population sample maybe due to ascertainment bias as a consequence of the strategyused to design the Illumina R© Bovine SNP50 BeadChip. In thisregard, many of the SNPs that constitute this first-generationSNP array were identified from heterozygous positions in theinbred Hereford female (L1 Dominette 01449) bovine genomeassembly or through comparisons of random shotgun readsfrom six diverse cattle breeds that were aligned directly to thesame Hereford genome assembly (Matukumalli et al., 2009). Thisapproach to SNP array design will inevitably lead to elevatedintrabreed genomic variation using the Illumina R© Bovine SNP50BeadChip with Hereford cattle (Meuwissen, 2009) and accountsfor the dispersed pattern of individual HRFD samples inFigure 5.

Examination of Figures 4, 5 indicates that two of the KY12animals sampled may exhibit a genetic signature of ancestralcrossbreeding with another cattle population, which, anecdotally,is likely to have been due to crossbreeding with Angus cattle.Therefore, another PCA plot was generated (SupplementaryFigure 1) that shows PC1 and PC2 for individual animals fromthe KY92, KY12, ANGU and RANGpopulation samples. The two

FIGURE 4 | Principal component analysis plot constructed for PC1 and PC2 from genome-wide SNP data (36,621 autosomal SNPs) for the EU data set of 605

individual animals. The smaller histogram plot shows the relative variance contributions for the first 10 PCs and PC1 and PC2 account for 18.2% and 16.8% of the

total variation for PC1–10, respectively.

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FIGURE 5 | Principal component analysis plot constructed for PC1 and PC2 from genome-wide SNP data (37,395 autosomal SNPs) for the BI data set of 351

individual animals. The smaller histogram plot shows the relative variance contributions for the first 10 PCs and PC1 and PC2 account for 23.7% and 22.7% of the

total variation for PC1–10, respectively.

animals exhibiting a genetic signature of possible crossbreeding(KY12_06 and KY12_58) are indicated on SupplementaryFigure 1. Notwithstanding the KY12_06 and KY12_58 datapoints, the genetic similarity among all Kerry cattle sampledis evident by comparison of the tight KY92 and KY12 samplecluster to the dispersion of the ANGU and RANG samples onthe PCA plot in Supplementary Figure 1.

The values for the variation accounted for by PC3, PC4and PC5 in Figure 4 (EU data set) are relatively high (15.7,13.8, and 10.4%, respectively). For Figure 5 (BI data set), thevariation accounted for by PC3 is also relatively high (19.1%).Therefore, we generated additional PCA plots of PC1 for each ofthe two data sets vs. these additional principal components (seeSupplementary Figures 2–5).

Analysis of Genetic Structure UsingGenome-Wide SNP DataThe results of the fastSTRUCTURE analyses using the EU andBI data sets are shown in Figures 6, 7, respectively. For bothanalyses, the Kerry cattle (KY92 and KY12) cluster as a singlegroup at K = 2 and are differentiated from all other Europeanor British and Irish cattle breeds. The other breed group thatis clearly differentiated at K = 2 in Figure 7 is the clustercomposed of the ANGU and RANG breeds. These results mirrorthe pattern shown for PC1 in Figure 5, and again emphasisethe genetic distinctiveness of Kerry cattle compared to otherEuropean production and landrace heritage breeds. Using thechooseK.py script the “true” number of clusters corresponding to

the likely number of ancestral populations was estimated to bebetween 12 and 14 for the EU data set and either 7 or 8 for the BIdata set.

For both data sets, animals from the KY12 population sampleappear to be more genetically homogenous compared to theKY92 population sample. This observationmay be a consequenceof increasing use, since the early 1990s, of small numbers ofartificial insemination (AI) Kerry sires. It is also noteworthythat the two individual animals detected with a substantialsignature of putative historical crossbreeding (KY12_06 andKY12_58) show marked patterns of population admixture in thefastSTRUCTURE results, which are indicated by red arrows inFigures 6, 7.

Modelling Historical Effective PopulationSize (Ne) Using Genome-Wide SNP DataThe results from modelling historical Ne in a selection ofproduction and heritage cattle breeds and populations (KY92,KY12, DXTR, BSHN, BGAL, LNCR, ANGU, JRSY, and HOLS)are provided in Supplementary Table 2 and visualised inFigure 8. The “demographic fingerprints” (Barbato et al., 2015)of the two Kerry populations shown in Figure 8 and tabulatedin Supplementary Table 2 are more similar to those of theproduction breeds with large census populations (BSHN, ANGU,JRSY, HOLS) than the other heritage breeds with relativelysmall census population sizes (DXTR, BGAL, LNCR). The KY92,KY12, BSHN, ANGU, JRSY, and HOLS populations show adeclining trend from historicalNe peaks between 1,500 and 2,000

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FIGURE 6 | Hierarchical clustering of individual animals using genome-wide SNP data (36,621 autosomal SNPs) for the EU data set of 605 individual animals. Results

are shown for modelled ancestral populations K = 2–14. The cluster numbers corresponding to the likely number of ancestral populations are highlighted with a light

red overlay and the two outlier Kerry samples (KY12_06 and KY12_58) are indicated with red arrows.

more than 900 generations ago to Ne values estimated to be lessthan 200 within the last 20 generations. One the other hand,the DXTR, BSHN, BGAL and LNCR populations display a moresevere decline from historical Ne peaks between 2,500 and 4,000more than 900 generations ago to Ne values estimated to be lessthan 150 within the last 20 generations.

It is important to keep in mind these Ne trends may bepartly a consequence of the relatively small sample sizes for theDXTR, BGAL, and LNCR breeds (see Table 2), coupled withdifferent histories of migration, gene flow and, in particular,strong artificial selection in the production cattle populations.Notwithstanding these caveats, the most recent modelled Ne

values for the KY92 and KY12 population samples are 89 and88, respectively. These values are Ne estimates for 12 generationsin the past and assuming a generation interval of between 4 and6 years, which is based on a pedigree estimate from a similarheritage cattle population of 5.66 (Mészáros et al., 2015), thiscorresponds to between 48 and 72 years before 2012 (for theKY12 population). This is approximately the period between1940 and 1965, which is during the time that the Kerry breedstarted to decline precipitously in census population size and also

Ne estimated from herd book data (O’hUigín and Cunningham,1990; Food and Agriculture Organization, 2007).

From a conservation perspective, livestock populationsgenerally exhibit Ne values relative to total census populationsizes (Nc) that are substantially lower than seen in comparablewild mammal populations (Hall, 2016). Also, estimation of Ne

using methods such as SNeP that leverage genome-wide SNPlinkage disequilibrium (LD) data will tend to underestimate Ne

because of physical linkage between many of the SNPs in the dataset (Waples et al., 2016). Nevertheless, taking this into account,there is still cause for concern that the most recent Ne valuesmodelled for the KY92 and KY12 population samples are belowthe critical Ne threshold of 100 recommended by Meuwissen(2009) for long-term viability of discrete livestock breeds andpopulations.

Genomic Relationship and Analysis ofInbreedingSupplementary Table 1 shows a genomic relationship matrix interms of genotype IBS for the genome-wide SNP data generatedfor individual animals in the KY92 and KY12 population

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FIGURE 7 | Hierarchical clustering of individual animals using genome-wide SNP data (37,395 autosomal SNPs) for the BI data set of 351 individual animals. Results

are shown for modelled ancestral populations K = 2–9. The cluster numbers corresponding to the likely number of ancestral populations are highlighted with a light

red overlay and the two outlier Kerry samples (KY12_06 and KY12_58) are indicated with red arrows.

samples. Close genomic relationship between individual animalssampled from the same herd is evident in the SNP genotypeIBS values between samples. In addition, the relatively lowgenomic relationship between the KY12_06 and KY12_58 outliersamples (Figures 4–7) and the rest of the Kerry cattle sampledis also evident in Supplementary Table 1. These data emphasisethe value of intrapopulation genomic relationship values foridentifying animals (in this case, KY12_06 and KY12_58) thatshould not be used in breeding programmes. They also highlightthe potential of genome-wide SNP data for providing a systematicapproach to prioritising males and females with minimumgenomic relationship for breeding to minimize loss of geneticdiversity and maintain or increase Ne (Gandini et al., 2004;Meuwissen, 2009; de Cara et al., 2011, 2013).

Genome-wide autozygosity estimated from SNP data usingFROH and the FROH statistic are visualised in Figure 9 forindividual animals from the KY92 and KY12 populationsamples and a range of European comparator breeds. Additionalsummary ROH data is provided in Supplementary Table 3and also Supplementary Figure 6, which reveals marked

inter-population differences in ROH length and demonstratesthat the SNP density of the Illumina R© Bovine SNP50 BeadChipis too low to reliably capture ROH below 5Mb in length, anobservation previously reported by Purfield et al. (2012).

There is significant variation in FROH values among individualanimals and between breeds and populations. The non-parametric Wilcoxon rank sum test was performed on FROHdistributions for all pairwise population/breed comparisons withapplication of the Bonferroni correction P-value adjustment formultiple statistical tests (Supplementary Table 4). This analysisdemonstrated that the KY12 population sample exhibited asignificantly higher mean FROH value than the KY92 populationsample (0.098 vs. 0.079; Padjust = 0.0081). This is importantfrom a conservation genetics perspective, indicating thatgenome-wide autozygosity, which is highly correlated withconventional pedigree-based estimates of inbreeding (FPED) forcattle (Purfield et al., 2012; Ferencakovic et al., 2013; Martikainenet al., 2017), has increased for the Kerry cattle populationin the 20 years between sampling of the KY92 and KY12populations.

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FIGURE 8 | Genetic effective population size (Ne) trends modelled using genome-wide SNP data. Results for the KY92 and KY12 populations are shown with seven

comparator heritage and production cattle breeds.

The importance of understanding and quantifying genome-wide autozygosity for genetic conservation purposes has recentlybeen highlighted through correlation of FROH with inbreedingdepression for a range of production traits in domestic cattle(Bjelland et al., 2013; Pryce et al., 2014; Kim et al., 2015).Importantly, FROH has also been shown to correlate withinbreeding depression for bovine fertility traits in both males(Ferencakovic et al., 2017) and females (Kim et al., 2015;Martikainen et al., 2017). Finally, according to basic populationgenetic principles, recent inbreeding captured by FROH willlead to recessive deleterious genomic variants emerging at apopulation level—a phenomenon that has been studied in bothhumans and cattle (Szpiech et al., 2013; Zhang et al., 2015).

Genome-Wide Signatures of Selection inthe Kerry Cattle BreedThe results of the genome-wide scan for signatures of selectionusing the CSS method in the Kerry cattle breed are shownin Figure 10. Six distinct selection signatures were detected onBTA9, BTA12, BTA16, BTA17, BTA19, and BTA28. A total of178 genes were located within the genomic ranges ± 1.0Mb ofselection peaks and 32 of these genes were located within theboundaries of a selection peak. Supplementary Table 5 providesdetailed information for these 178 genes.

A single gene was located within the BTA9 selection peak—thephosphodiesterase 7B gene (PDE7B), which has been associatedwith neurobiological processes (de Gortari and Mengod, 2010)and has been previously linked to genetic changes associatedwith dog (Canis lupus familiaris) domestication and behaviour(Freedman et al., 2016). A single gene was also located

within the BTA16 selection peak—the dorsal inhibitory axonguidance protein gene (DRAXIN), which encodes a proteinthat regulates axon guidance, neural circuit formation andvertebrate brain development (Islam et al., 2009; Shinmyoet al., 2015). Twenty-four genes were located within the BTA17selection peak, including BICDL1, RAB35, and RNF10, whichhave been associated with neurobiology and brain development(Hoshikawa et al., 2008; Schlager et al., 2010; Villarroel-Camposet al., 2016) and SIRT4 and COQ5 that function in cellularmetabolism (Kawamukai, 2015; Elkhwanky and Hakkola, 2017).Six genes were located within the BTA28 selection peak,including, most notably, the Rho GTPase activating protein 22gene (ARHGAP22), which has recently been associated withbovine fertility as an mRNA expression biomarker for oocytecompetence in cumulus cells (Melo et al., 2017).

To obtain a broader perspective on natural and artificialselection acting at a population level on the Kerry cattle genome,a functional gene set enrichment approach (GSEA) was takenusing IPA with the 178 genes located within ± 1.0Mb of eachselection peak (Supplementary Table 5). Of these 178 genes, 141could be mapped to the IPA knowledgebase and the summaryresults for the IPA Physiological System Development andFunction category are shown in Supplementary Table 6, revealingan enrichment of biological processes associated with nervoussystem development and behaviour. This functional enrichmentcoupled with the neurobiologically relevant single-gene selectionpeaks on BTA9 (PDE7B) and BTA16 (DRAXIN) suggests thatnatural and/or artificial selection related to brain developmentand behaviour has been important in the microevolution of theKerry cattle breed. In this regard, it is therefore noteworthy

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FIGURE 9 | Tukey box plots showing the distributions of FROH values estimated with genome-wide SNP data for the KY92 and KY12 populations and nine

comparator heritage and production cattle breeds.

that Kerry cattle, including bulls, are recognised as beingcomparatively docile and easy to manage (Curran, 1990).

Genomics, Genetic Distinctiveness andMicroevolution of Kerry Cattle:Implications for Breed Management andGenetic ConservationThe genome-wide phylogenetic and population genetic analysesdetailed here demonstrate that Kerry cattle represent animportant farm animal genetic resource, befitting the breed’sstatus as a livestock population with a unique history ofadaptation to the climate and physical geography of southwestIreland at the edge of Western Europe. Notably, from agenetic conservation and breed management perspective, high-resolution comparative PCA (Figures 4, 5) and genetic clusteringresults (Figures 6, 7) demonstrate that Kerry cattle are markedlydistinct from other British and European cattle populations.This observation may also be placed in the context of recentpaleogenomic studies that have detected ancient gene flowfrom wild British aurochs (B. primigenius) into the ancestorsof present-day Kerry cattle (Orlando, 2015; Park et al., 2015;Upadhyay et al., 2017).

The current genetic status of the Kerry cattle populationis underlined by analyses of genetic effective population size(Ne) and inbreeding using genome-wide SNP data. As shown inTable 2, genome-wide observed heterozygosity (Ho) is relativelyhigh in the KY92 and K12 population samples, particularly

for endangered heritage cattle breeds. However, it has beenlong recognised that monitoring Ne is a more important toolfor rational breed management and long-term conservationof endangered livestock populations (Notter, 1999; Gandiniet al., 2004; Biscarini et al., 2015). As shown in Figure 8

and Supplementary Table 2, the Kerry cattle population has arecent demographic trend of Ne decline, to the point where themost recent modelled Ne values are below the recommendedthreshold for sustainable breed management and conservation(Meuwissen, 2009). There is also cause for concern that genomicinbreeding estimated using genome-wide autozygosity (FROH)and visualised in Figure 9 has increased significantly in the 20-year period between the sampling of the KY92 and KY12 Kerrycattle populations.

In a more positive light, as shown in the present study,detection of discrete signatures of selection using the relativelylow-density Illumina R© Bovine SNP50 BeadChip is encouragingfor wider studies of genome-wide microevolution in endangeredheritage livestock populations such as Kerry cattle. Futuresurveys of heritage livestock populations that use higher-densitySNP array platforms and ultimately whole-genome sequence datacould provide exquisitely detailed information on the genomicregions and associated polygenic production, health, fertility andbehavioural traits shaped, over many centuries, by the agro-ecology and pre-industrial farming systems of southwest Ireland.

In conclusion, the results presented here for the Kerry cattlepopulation demonstrate that population genomics analyses oflarge SNP data sets can provide useful information concerning

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FIGURE 10 | Manhattan plots of composite selection signal (CSS) results for Kerry cattle (n = 98) contrasted with EU cattle (n = 102). (A) Unsmoothed results.

(B) Smoothed results obtained by averaging CSS of SNPs within each 1Mb window. Red dotted line on each plot denotes the genome-wide 0.1% threshold for the

empirical CSS scores. Red vertical arrows indicate selection peaks detected on BTA09, BTA12, BTA16, BTA17, BTA19, and BTA28.

the microevolution and recent genetic history of heritagelivestock breeds. In particular, we would recommend thatcomparable surveys in other populations consider the use ofgenome-wide scans for signatures of selection, which can providea functional genomics perspective on evolutionary adaptations toparticular agricultural environments and production systems.

DATA ACCESSIBILITY

The Illumina R© Bovine SNP50 BeadChip SNP genotype data forthe Kerry cattle KY92 and KY12 population samples generatedfor this study are available from the Dryad Digital Repository:https://doi.org/10.5061/dryad.8fk81.

ETHICS STATEMENT

With the exception of Kerry cattle sampled during 2011–12,all samples and data was obtained from previously publishedscientific studies. The re-use of these samples and data isconsistent with the 3Rs principles on replacement, refinementand reduction of animals in research (www.nc3rs.org.uk/the-3rs). For the 2011-12 Kerry cattle, population owners’ consentto sample DNA for research was obtained and individual ownersconducted sampling of animals using non-invasive nasal swabs.In this regard, scientific animal protection in Ireland is subject

to European Union Directive 2010/63/EU, which states that theDirective does not apply to “practices not likely to cause pain,suffering, distress or lasting harm equivalent to, or higher than,that caused by the introduction of a needle in accordance withgood veterinary practice.”

AUTHOR CONTRIBUTIONS

DEM, DAM, AF, and JK conceived and designed the project;DEM, IWR, DAM, AF, and JK organised sample collection andgenotyping; SB, GM, IWR, DAM, SP, CC, IASR, and DEMperformed the analyses; SB and DEM wrote the manuscript andall authors reviewed and approved the final manuscript.

FUNDING

This work was supported by Department of Agriculture, Foodand the Marine (DAFM) funding under the Genetic Resourcesfor Food and Agriculture scheme (grant no: 10/GR/06); anInvestigator Programme Grant from Science Foundation Ireland(SFI/08/IN.1/B2038); a Research Stimulus Grant from DAFM(RSF 06 406); a European Union Framework 7 Project Grant(KBBE-211602-MACROSYS); the Brazilian Science WithoutBorders Programme (CAPES grant no. BEX-13070-13-4) and theUCDMSc Programme in Evolutionary Biology.

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ACKNOWLEDGMENTS

The authors wish to express their gratitude to the Kerry CattleSociety (www.kerrycattle.ie) for facilitating and encouraging thisproject. In particular, we thank Rosemary and Jeremy Hill,Matthew English Hayden, Jeanne Hendrick and the Irish CattleBreeding Federation (ICBF – www.icbf.com) for expert guidanceand assistance with animal sourcing and DNA sampling. Weare also grateful to Professor Dan Bradley (Smurfit Institute ofGenetics, Trinity College Dublin) for access to DNA sample

archives. In addition, we thank all of the Kerry cattle owners whoprovided access to animals, samples and pedigree information.Finally, we thank Weatherbys Scientific for provision of SNParray genotyping services (www.weatherbysscientific.com).

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be foundonline at: https://www.frontiersin.org/articles/10.3389/fgene.2018.00051/full#supplementary-material

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Conflict of Interest Statement: The authors IWR and SP are employed byIdentiGEN, Ltd.

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